Monitoring of the power system load margin based on a machine learning technique

Abstract

The voltage stability margin is an important load margin measure used in power system operating centers to prevent a voltage collapse. However, oscillatory problems that arise with increasing load can also compromise the performance and stability of the power system. Thus, it is essential to determine a load margin that meets the requirements for voltage stability and small-signal stability in dynamic security assessment. This article proposes to use a artificial neural network, a supervised machine learning technique, to predict the load margin range of the power system considering the requirements of voltage stability and small-signal stability and using data of electrical quantities of certain buses that have a phasor measurement unit. A direct method based on a power systems model that determines the load margin meeting the voltage and small-signal stability requirements will be applied to generate the database for the training and testing stages of artificial neural network. The sequential forward selection algorithm was used in this research to select the buses to have a phasor measurement unit. Case studies are presented and discussed to verify the proposed load margin monitoring system based on artificial neural networks.